关键词: 零样本常识问答, 知识图谱, 元学习

Abstract: Zero-shot commonsense question answering requires the model to answer unseen questions. Currently, most researchers inject knowledge graphs as commonsense knowledge. But when there is little overlap between the knowledge graph and the target dataset in terms of domains, increasing either the types of knowledge graphs or the number of triples within the graph cannot effectively enhance the question answering ability of the model on the target dataset. To address these limitations, this paper proposes a method called multi-knowledge meta-learning (MKML) for zero-shot commonsense question answering. This method trains different KG-Adapters to inject multiple knowledge graphs into the pretrained model separately and merges the knowledge from these adapters by constructing a Meta-MoE module. At the same time, in order to enhance the ability of the model to answer unknown target domain questions based on its own knowledge, MKML updates the parameters of Meta-MoE through the construction of a multi-source meta-learning method. This helps the model acquire shared knowledge structure distribution information and enables it to identify unknown domain knowledge distributions based on the question prompts, thus quickly adapting to the target dataset. Experimental results on multiple commonsense question answering datasets demonstrate that compared to eight existing baseline methods, MKML exhibits higher accuracy in zero-shot commonsense question answering.

Key words: zero-shot commonsense question answering, knowledge graph, meta-learning